模型名字	模型ID	调用接口
yolov3.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV3	CVI_TDL_Detection
yolov5m.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV5	CVI_TDL_Detection
yolov5s.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV5	CVI_TDL_Detection
yolov6m.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV6	CVI_TDL_Detection
yolov6s.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV6	CVI_TDL_Detection
yolox_m.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOX	CVI_TDL_Detection
yolox_s.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOX	CVI_TDL_Detection
yolov7-tiny.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV7	CVI_TDL_Detection
yolov8n.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV8_DETECTION	CVI_TDL_Detection
yolov8s.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV8_DETECTION	CVI_TDL_Detection
ppyoloe.cvimodel	CVI_TDL_SUPPORTED_MODEL_PPYOLOE	CVI_TDL_Detection
yolov10n.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV10_DETECTION	CVI_TDL_Detection
hardhat_detection_v2.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV8_HARDHAT	CVI_TDL_Detection
hand_det_qat_640x384.cvimodel	CVI_TDL_SUPPORTED_MODEL_HAND_DETECTION	CVI_TDL_Detection
pet_det_640x384.cvimodel	CVI_TDL_SUPPORTED_MODEL_PERSON_PETS_DETECTION	CVI_TDL_Detection
yolov8n_384_640_person_vehicle.cvimodel	CVI_TDL_SUPPORTED_MODEL_PERSON_VEHICLE_DETECTION	CVI_TDL_Detection
meeting_det_640x384.cvimodel	CVI_TDL_SUPPORTED_MODEL_HAND_FACE_PERSON_DETECTION	CVI_TDL_Detection
yolov8n_headperson.cvimodel	CVI_TDL_SUPPORTED_MODEL_HEAD_PERSON_DETECTION	CVI_TDL_Detection
mobiledetv2-d0-ls.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_COCO80	CVI_TDL_Detection
mobiledetv2-d1-ls.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_COCO80	CVI_TDL_Detection
mobiledetv2-d2-ls.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_COCO80	CVI_TDL_Detection
mobiledetv2-vehicle-d0-ls.cvimode	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_VEHICLE	CVI_TDL_Detection
mobiledetv2-pedestrian-d0-ls-384.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_PEDESTRIAN	CVI_TDL_Detection
mobiledetv2-pedestrian-d0-ls-448.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_PEDESTRIAN	CVI_TDL_Detection
mobiledetv2-pedestrian-d0-ls-640.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_PEDESTRIAN	CVI_TDL_Detection
mobiledetv2-pedestrian-d0-ls-768.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_PEDESTRIAN	CVI_TDL_Detection
mobiledetv2-pedestrian-d1-ls.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_PEDESTRIAN	CVI_TDL_Detection
mobiledetv2-pedestrian-d1-ls-1024.cvimodel	CVI_TDL_SUPPORTED_MODEL_MOBILEDETV2_PEDESTRIAN	CVI_TDL_Detection
retinaface_mnet0.25_342_608.cvimodel	CVI_TDL_SUPPORTED_MODEL_RETINAFACE	CVI_TDL_FaceDetection
retinaface_mnet0.25_608_342.cvimodel	CVI_TDL_SUPPORTED_MODEL_RETINAFACE	CVI_TDL_FaceDetection
retinaface_mnet0.25_608.cvimodel	CVI_TDL_SUPPORTED_MODEL_RETINAFACE	CVI_TDL_FaceDetection
scrfd_320_256_ir.cvimodel	CVI_TDL_SUPPORTED_MODEL_SCRFDFACE	CVI_TDL_FaceDetection
scrfd_480_270_int8.cvimodel	CVI_TDL_SUPPORTED_MODEL_SCRFDFACE	CVI_TDL_FaceDetection
scrfd_480_360_int8.cvimodel	CVI_TDL_SUPPORTED_MODEL_SCRFDFACE	CVI_TDL_FaceDetection
scrfd_500m_bnkps_432_768.cvimodel	CVI_TDL_SUPPORTED_MODEL_SCRFDFACE	CVI_TDL_FaceDetection
retinafaceIR_mnet0.25_342_608.cvimodel	CVI_TDL_SUPPORTED_MODEL_RETINAFACE_IR	CVI_TDL_FaceDetection
retinafaceIR_mnet0.25_608_342.cvimodel	CVI_TDL_SUPPORTED_MODEL_RETINAFACE_IR	CVI_TDL_FaceDetection
retinafaceIR_mnet0.25_608_608.cvimodel	CVI_TDL_SUPPORTED_MODEL_RETINAFACE_IR	CVI_TDL_FaceDetection
retinaface_yolox_fdmask.cvimodel	CVI_TDL_SUPPORTED_MODEL_FACEMASKDETECTION	CVI_TDL_FaceDetection
pipnet_blurness_v5_64_retinaface_50ep.cvimodel	CVI_TDL_SUPPORTED_MODEL_FACELANDMARKERDET2	CVI_TDL_FaceLandmarkerDet2
fqnet-v5_shufflenetv2-softmax.cvimodel	CVI_TDL_SUPPORTED_MODEL_FACEQUALITY	CVI_TDL_FaceQuality
mask_classifier.cvimodel	CVI_TDL_SUPPORTED_MODEL_MASKCLASSIFICATION	CVI_TDL_MaskClassification
cviface-v5-m.cvimodel	CVI_TDL_SUPPORTED_MODEL_FACERECOGNITION	CVI_TDL_FaceRecognition
cviface-v6-s.cvimodel	CVI_TDL_SUPPORTED_MODEL_FACERECOGNITION	CVI_TDL_FaceRecognition
face_attr_112_112.cvimodel	CVI_TDL_SUPPORTED_MODEL_FACEATTRIBUTE_CLS	CVI_TDL_FaceAttribute_cls
hand_cls_128x128.cvimodel	CVI_TDL_SUPPORTED_MODEL_HANDCLASSIFICATION	CVI_TDL_HandClassification
hand_kpt_128x128.cvimodel	CVI_TDL_SUPPORTED_MODEL_HAND_KEYPOINT	CVI_TDL_HandKeypoint
hand_kpt_cls9.cvimodel	CVI_TDL_SUPPORTED_MODEL_HAND_KEYPOINT_CLASSIFICATION	CVI_TDL_HandKeypointClassification
wpodnet_v0_bf16.cvimodel	CVI_TDL_SUPPORTED_MODEL_WPODNET	CVI_TDL_LicensePlateDetection
lprnet_v0_tw_bf16.cvimodel	CVI_TDL_SUPPORTED_MODEL_LPRNET_TW	CVI_TDL_LicensePlateDetection_TW
lprnet_v1_cn_bf16.cvimodel	CVI_TDL_SUPPORTED_MODEL_LPRNET_CN	CVI_TDL_LicensePlateDetection_CN
ir_liveness.cvimodel	CVI_TDL_IrLiveness	CVI_TDL_SUPPORTED_MODEL_IRLIVENESS
yolov8n_pose_384_640.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV8POSE	CVI_TDL_PoseDetection
simcc_mv2_pose.cvimodel	CVI_TDL_SUPPORTED_MODEL_SIMCC_POSE	CVI_TDL_PoseDetection
yolov8n_seg.cvimodel	CVI_TDL_SUPPORTED_MODEL_YOLOV8_SEG	CVI_TDL_YoloV8_Seg
face_landmark_bf16.cvimodel	CVI_TDL_FaceLandmarker	CVI_TDL_SUPPORTED_MODEL_FACELANDMARKER
eye_v1_bf16.cvimodel	CVI_TDL_EyeClassification	CVI_TDL_SUPPORTED_MODEL_EYECLASSIFICATION
yawn_v1_bf16.cvimodel	CVI_TDL_YawnClassification	CVI_TDL_SUPPORTED_MODEL_YAWNCLASSIFICATION
c10_lightv2_mse40_mix.cvimodel	CVI_TDL_SUPPORTED_MODEL_SOUNDCLASSIFICATION	CVI_TDL_SoundClassification

模型名字	ION/FlASH	推理耗时（ms）	模型性能
c10_lightv2_mse40_mix.cvimodel	0.62 MB/587KB	3.12	Acc: 98.9%
cviface-v6-s.cvimodel	2.97 MB/2.36MB	7.96	FMR:0.1 FNMR:0.0141
hand_det_qat_640x384.cvimodel	2.82MB/913KB	16.6	mAP(0.5): 82.1%
hand_kpt_128x128.cvimodel	0.84MB/809KB	0.829	pck@0.05:0.886
hand_kpt_cls9.cvimodel	0.05MB/54KB	0.255	Acc: 91.0%
hardhat_detection_v2.cvimodel	7.48MB/1.05MB	38.4	mAP(0.5)=92.42%
mask_classifier.cvimodel	3.11MB/2. 30MB	4.88	Acc: 97.2%
mobiledetv2-pedestrian-d0-ls-448.cvimodel	2.12MB/441KB	10.3	mAP(0.5): 66.4%
pet_det_640x384.cvimodel	6.5MB/2.99MB	32.8	mAP(0.5):87.0%
ppyoloe.cvimodel	14.55MB/8.9MB	101.15	mAP(0.5): 55.4%
scrfd_768_432_int8_1x.cvimodel	4.50 MB/742KB	10.9	mAP(0.5)：easy: 89.4% medium: 86.5% hard:65.9%
yolov7-tiny.cvimodel	70.66MB/7.7MB	70.41	mAP(0.5): 53.4%
yolov8n_384_640_person_vehicle.cvimodel	5.58 MB/3.13M	28.5	mAP(0.5)：72.0%
yolov8n.cvimodel	31.56MB/3.5MB	45.62	mAP(0.5): 51.2%
yolov8n_headperson.cvimodel	5.29M/3.13M	26.5	mAP(0.5): 78.5%
yolox_s.cvimodel	95.44MB/10.0MB	127.91	mAP(0.5): 52.4%
模型评测信息待更新